Department of Chemical and Materials Science Engineering Seminar Series

Hongliang Xin, Virginia Tech

Modern science is entering a new form of alchemy, not mystical transmutation but the transformation of knowledge into discovery. In this talk, I will use electrocatalysis for sustainable fuels and chemicals as a lens to examine how domain theory, high-performance computing, and experimentation can be integrated with machine learning and AI for scientific discovery. The discussion begins at electrified interfaces, where site structures, microenvironment, and solvent dynamics determine catalytic outcomes. I will describe how atomistic simulations, electrokinetic modeling, and large-scale machine-learned interatomic potentials can help us understand interfacial charge-transfer reactions, and how theory-infused deep learning can translate them into interpretable design principles beyond conventional methods.

Steven Weinman, University of Alabama

Polyamide reverse osmosis (RO) membranes have been the gold standard for water desalination for over 30 years. Even though RO membranes exhibit excellent performance rejecting salt ions, they do not reject small, neutral molecules (SNMs) at adequate levels. Due to the fast, uncontrolled nature of the interfacial polymerization (IP) reaction used to fabricate polyamide RO membranes, the polyamide layer contains both crosslinked and un-crosslinked free volume holes (pores). Because SNMs are not affected by the charge exclusion rejection mechanism that allows for high salt rejection, reducing the free volume to reduce the passage of SNMs through the membrane is needed. Surfactants regulate IP via their self-assembled network to alter the behavior between the aqueous and organic phases. This leads to the formation of more permselective membranes with a more uniform polyamide density distribution. In this study, different anionic, cationic, and non-ionic surfactants have been employed during IP for polyamide membrane synthesis. Surfactant interfacial behavior was characterized, microscale visualization using the pendant drop was analyzed, and polyamide membranes were synthesized, characterized, and performance evaluated to understand how surfactants influence a variety of membrane properties. These results will help us synthesize better performing RO membranes for new applications.

Paul Rottmann, Assistant Professor of Materials Engineering at University of Kentucky

In this presentation, I will detail the results from three ongoing research projects. First, I will present the influence of part geometry on the microstructure and properties of additively manufactured Ni (IN718) and steel (17-4PH) alloys, with a focus on the fundamental reasons why IN718 is significantly more geometry-sensitive than 17-4PH when thin-walled sections are necessary. Second, I will discuss recent developments in utilizing an applied magnetic field to accelerate the development of strengthening precipitates in 7000 series aluminum alloys, substantially reducing the annealing time required to reach desired strength levels. Third, I will present novel in situ methodologies we are developing to characterize the 3D deformation of carbon-based thermal protection and structural materials for space applications.

Alexandra (Zan) Paterson, Assistant Professor of Materials & Electrical Engineering at University of Kentucky